Big data could revolutionise transport, right now

Transport data, the old fashioned way. Image: Getty.

The future of transport appears full of fun and flashy possibilities. From super-fast hyperloop transport systems, to self-driving cars and hovering taxis, new technology promises to move us further and faster than ever before. Yet for cities facing everyday problems such as congestion, air pollution and under capacity, the most effective solution could be the humble bus – coupled with the power of data. The Conversation

Of course, in many cities, technology has already begun replacing printed timetables with live departure boards, using real-time data about buses’ locations sourced from GPS monitoring. But this is just the beginning. There’s one source of data which could offer a live overview of a city’s entire transport network without a single penny of investment. And you’ve probably got it on you right now.

Modern mobile phones contain an array of sensors, including GPS, accelerometer, gyroscope, digital compass and more, which are capable of producing a constant stream of data. Individual units of movement, tracked by a phone’s GPS and processed on mass, can give detailed information on journey times, speed and destinations.

Fair trade

Of course, using this data without compromising users’ privacy is a challenge. When dealing with location information, anonymisation can only take you so far. But there is a neat solution. In exchange for their data, passengers could receive a wealth of benefits, including more flexible routes and timetables, predictive of need at any given hour. The level of service could be directly linked to the amount of data a passenger chooses to share.

By combining these data with efficient ticketing across a range of transport modes, including bus, tram, train, taxi and others, it would be possible to create a flexible and responsive system, which can tailor transport solutions to every person’s needs.

Individuals would be able to dial in their destination as they leave home, to be guided by the fastest, cheapest, healthiest or most environmentally friendly route to their destination on a given day, by whatever means, at a standard unit of price per distance. The routes would be responsive to changing weather and road closures, with flexible timetables and services, to cater for a wet Tuesday when everyone wants to take the bus rather than walk or cycle. Overcrowding could be reduced by balancing the load of commuters across different modes of transport.

Breath in. Image: Emily Lindsay Brown/author provided.

The best thing is, the system would constantly be learning and improving. It is relatively straightforward to automatically schedule extra services in real time if, say, there’s an unusually large number of people waiting at a particular stop. But, with sophisticated machine learning, which processes large amounts of historical data to detect patterns, slumps and hikes in demand could be preempted. Allowing a transport network to self-learn using data from its consumers can help it to evolve a better service, while maintaining the modest margins of the provider.

The transport system can also be used as a tool to promote social good. For one thing, price can be used as a powerful influence for positive behavioural change: discounts could be offered for getting off a stop earlier and walking the remaining distance. The bus or tram itself can also be enhanced by making it a place for culture, education and information. Advertising could be complemented or even replaced by community television, public art and educational information, which offer a more positive experience for the captive audience.


Here today?

All of this potential can be unlocked today: not in the future, but in the here and now. The main challenges are overcoming tradition, using a single ticket across various transport modes and apportioning revenue between a complex tapestry of transport providers within the domain of a single transport authority.

Alongside Bournemouth University, a small digital technology company, We Are Base, is attempting to do exactly that. Together, we are finding ways to leverage data to make public transport a better option than private vehicles in terms of punctuality, flexibility and comfort. We are also collecting and analysing real-time data to demonstrate how a transport network could use machine learning to optimise its customer transport efficiency.

The technology is the relatively easy part; negotiating local politics often proves more difficult. For instance, finding a fair way of distributing ticket revenues among operators involved in a journey which uses more than one mode of transport, potentially across a number of zones and boroughs. Gaining consumer trust is also essential. For such systems to work, the consumer must choose to follow journey suggestions, even though they might not seem to be optimal at the time. This is particularly difficult; after all, how many of us can say that we trust our local bus companies when some still struggle to run the services to a static timetable?

The opportunity for a transport revolution is here – but for it to work it must be aspired to. This starts with consumers and local authorities understanding and seeing the benefits of a self-learning, adaptable and truly flexible local transport system. And given that it’s within reach, they shouldn’t put up with anything less. So, next time someone proposes a flashy new solution to transport woes, just remember that true innovation lies in the hands of the commuters themselves – locked inside their mobile phones.

Marcin Budka is principal academic in data science, and Manuel Martin Salvador a PhD Candidate, at Bournemouth University.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Urgently needed: Timely, more detailed standardized data on US evictions

Graffiti asking for rent forgiveness is seen on a wall on La Brea Ave amid the Covid-19 pandemic in Los Angeles, California. (Valerie Macon/AFP via Getty Images)

Last week the Eviction Lab, a team of eviction and housing policy researchers at Princeton University, released a new dashboard that provides timely, city-level US eviction data for use in monitoring eviction spikes and other trends as Covid restrictions ease. 

In 2018, Eviction Lab released the first national database of evictions in the US. The nationwide data are granular, going down to the level of a few city blocks in some places, but lagged by several years, so their use is more geared toward understanding the scope of the problem across the US, rather than making timely decisions to help city residents now. 

Eviction Lab’s new Eviction Tracking System, however, provides weekly updates on evictions by city and compares them to baseline data from past years. The researchers hope that the timeliness of this new data will allow for quicker action in the event that the US begins to see a wave of evictions once Covid eviction moratoriums are phased out.

But, due to a lack of standardization in eviction filings across the US, the Eviction Tracking System is currently available for only 11 cities, leaving many more places facing a high risk of eviction spikes out of the loop.

Each city included in the Eviction Tracking System shows rolling weekly and monthly eviction filing counts. A percent change is calculated by comparing current eviction filings to baseline eviction filings for a quick look at whether a city might be experiencing an uptick.

Timely US eviction data for a handful of cities is now available from the Eviction Lab. (Courtesy Eviction Lab)

The tracking system also provides a more detailed report on each city’s Covid eviction moratorium efforts and more granular geographic and demographic information on the city’s evictions.

Click to the above image to see a city-level eviction map, in this case for Pittsburgh. (Courtesy Eviction Lab)

As part of their Covid Resource, the Eviction Lab together with Columbia Law School professor Emily Benfer also compiled a scorecard for each US state that ranks Covid-related tenant protection measures. A total of 15 of the 50 US states plus Washington DC received a score of zero because those states provided little if any protections.

CityMetric talked with Peter Hepburn, an assistant professor at Rutgers who just finished a two-year postdoc at the Eviction Lab, and Jeff Reichman, principal at the data science research firm January Advisors, about the struggles involved in collecting and analysing eviction data across the US.

Perhaps the most notable hurdle both researchers addressed is that there’s no standardized reporting of evictions across jurisdictions. Most evictions are reported to county-level governments, however what “reporting” means differs among and even within each county. 

In Texas, evictions go through the Justice of the Peace Courts. In Virginia they’re processed by General District Courts. Judges in Milwaukee are sealing more eviction case documents that come through their courtroom. In Austin, Pittsburgh and Richmond, eviction addresses aren’t available online but ZIP codes are. In Denver you have to pay about $7 to access a single eviction filing. In Alabama*, it’s $10 per eviction filing. 

Once the filings are acquired, the next barrier is normalizing them. While some jurisdictions share reporting systems, many have different fields and formats. Some are digital, but many are images of text or handwritten documents that require optical character recognition programs and natural language processors in order to translate them into data. That, or the filings would have to be processed by hand. 

“There's not enough interns in the world to do that work,” says Hepburn.


Aggregating data from all of these sources and normalizing them requires knowledge of the nuances in each jurisdiction. “It would be nice if, for every region, we were looking for the exact same things,” says Reichman. “Instead, depending on the vendor that they use, and depending on how the data is made available, it's a puzzle for each one.”

In December of 2019, US Senators Michael Bennet of Colorado and Rob Portman of Ohio introduced a bill that would set up state and local grants aimed at reducing low-income evictions. Included in the bill is a measure to enhance data collection. Hepburn is hopeful that the bill could one day mean an easier job for those trying to analyse eviction data.

That said, Hepburn and Reichman caution against the public release of granular eviction data. 

“In a lot of cases, what this gets used for is for tenant screening services,” says Hepburn. “There are companies that go and collect these data and make them available to landlords to try to check and see if their potential tenants have been previously evicted, or even just filed against for eviction, without any sort of judgement.”

According to research by Eviction Lab principal Matthew Desmond and Tracey Shollenberger, who is now vice president of science at Harvard’s Center for Policing Equity, residents who have been evicted or even just filed against for eviction often have a much harder time finding equal-quality housing in the future. That coupled with evidence that evictions affect minority populations at disproportionate rates can lead to widening racial and economic gaps in neighborhoods.

While opening up raw data on evictions to the public would not be the best option, making timely, granular data available to researchers and government officials can improve the system’s ability to respond to potential eviction crises.

Data on current and historical evictions can help city officials spot trends in who is getting evicted and who is doing the evicting. It can help inform new housing policy and reform old housing policies that may put more vulnerable citizens at undue risk.

Hepburn says that the Eviction Lab is currently working, in part with the ACLU, on research that shows the extent to which Black renters are disproportionately affected by the eviction crisis.

More broadly, says Hepburn, better data can help provide some oversight for a system which is largely unregulated.

“It's the Wild West, right? There's no right to representation. Defendants have no right to counsel. They're on their own here,” says Hepburn. “I mean, this is people losing their homes, and they're being processed in bulk very quickly by the system that has very little oversight, and that we know very little about.”

A 2018 report by the Philadelphia Mayor’s Taskforce on Eviction Prevention and Response found that of Philadelphia’s 22,500 eviction cases in 2016, tenants had legal representation in only 9% of them.

Included in Hepburn’s eviction data wishlist is an additional ask, something that is rarely included in any of the filings that the Eviction Lab and January Advisors have been poring over for years. He wants to know the relationship between money owed and monthly rent.

“At the individual level, if you were found to owe $1,500, was that on an apartment that's $1,500 a month? Or was it an apartment that's $500 a month? Because that makes a big difference in the story you're telling about the nature of the crisis, right? If you're letting somebody get three months behind that's different than evicting them immediately once they fall behind,” Hepburn says.

Now that the Eviction Tracking System has been out for a week, Hepburn says one of the next steps is to start reaching out to state and local governments to see if they can garner interest in the project. While he’s not ready to name any names just yet, he says that they’re already involved in talks with some interested parties.

*Correction: This story initially misidentified a jurisdiction that charges $10 to access an eviction filing. It is the state of Alabama, not the city of Atlanta. Also, at the time of publication, Peter Hepburn was an assistant professor at Rutgers, not an associate professor.

Alexandra Kanik is a data reporter at CityMetric.